A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes

In large-scale group decision making (GDM), noncooperative behavior in the consensus reaching process (CRP) is not unusual. For example, some individuals might form a small alliance with the aim to refuse attempts to modify their preferences or even to move them against consensus to foster the alliance's own interests. In this paper, we propose a novel framework based on a self-management mechanism for noncooperative behaviors in large-scale CRPs (LCRPs). In the proposed consensus reaching framework, experts are classified into different subgroups using a clustering method, and experts provide their evaluation information, i.e., the multicriteria mutual evaluation matrices (MCMEMs), regarding the subgroups based on subgroups' performance (e.g., professional skills, cooperation, and fairness). The subgroups' weights are dynamically generated from the MCMEMs, which are in turn employed to update the individual experts' weights. This self-management mechanism in the LCRP allows penalizing the weights of the experts with noncooperative behaviors. Detailed simulation experiments and comparison analysis are presented to verify the validity of the proposed framework for managing noncooperative behaviors in the LCRP.